A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

Research advance in gas detection of volatile organic compounds released in rice quality deterioration process

Rice quality deterioration will cause grievous waste of stored grain and various food safety problems. Gas detection of volatile organic compounds (VOCs) produced by deterioration is a nondestructive detection method to judge rice quality and alleviate rice spoilage. This review discussed the research advance of VOCs detection in terms of nondestructive detection methods of rice quality deterioration, applications of VOCs in grain detection, inspection of characteristic gas produced during rice spoilage, rice deterioration prevention and control, and detection of VOCs released by rice mildew and insect attack. According to the main causes of rice quality deterioration and major sources of VOCs with off-odor generated during rice storage, deterioration can be divided into mold and insect infection. The results of literature manifested that researches mainly focused on the infection of Aspergillus in the mildew process and the attack of certain pests in recent years, thus the research scope was limited. In this paper, the gas detection methods combined with the chemometrics to qualitatively analyze the VOCs, as well as the correlation with the number of colonies and insects were further studied based on the common dominant strains during rice mildew, that is, Aspergillus and Penicillium fungi, and the common pests during storage, that is, Sitophilus oryzae and Rhyzopertha dominica. Furthermore, this paper pointed out that the quantitative determination of characteristic VOCs, the numeration relationship between VOCs and the degree of mildew and insect infestation, the further expansion of detection range, and the application of degraded rice should be the spotlight of future research.

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R² > 0.926, RMSEC < 0.481) compared with partial least-squares model (R² > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

Authentication of berries and berry-based food products

Berries represent one of the most important and high-valued group of modern-day health-beneficial "superfoods" whose dietary consumption has been recognized to be beneficial for human health for a long time. In addition to being delicious, berries are rich in nutrients, vitamins, and several bioactive compounds, including carotenoids, flavonoids, phenolic acids, and hydrolysable tannins. However, due to their high value, berries and berry-based products are often subject to fraudulent adulteration, commonly for economical gain, but also unintentionally due to misidentification of species. Deliberate adulteration often comprises the substitution of high-value berries with lower value counterparts and mislabeling of product contents. As adulteration is deceptive toward customers and presents a risk for public health, food authentication through different methods is applied as a countermeasure. Although many authentication methods have been developed in terms of fast, sensitive, reliable, and low-cost analysis and have been applied in the authentication of a myriad of food products and species, their application on berries and berry-based products is still limited. The present review provides an overview of the development and application of analytical chemistry methods, such as isotope ratio analysis, liquid and gas chromatography, spectroscopy, as well as DNA-based methods and electronic sensors, for the authentication of berries and berry-based food products. We provide an overview of the earlier use and recent advances of these methods, as well as discuss the advances and drawbacks related to their application.

The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey

The aim of this review is to describe the chromatographic, spectrometric, and spectroscopic techniques applied to honey for the determination of botanical and geographical origin and detection of adulteration. Based on the volatile profile of honey and using Solid Phase microextraction-Gas chromatography-Mass spectrometry (SPME-GC-MS) analytical technique, botanical and geographical characterization of honey can be successfully determined. In addition, the use of vibrational spectroscopic techniques, in particular, infrared (IR) and Raman spectroscopy, are discussed as a tool for the detection of honey adulteration and verification of its botanical and geographical origin. Manipulation of the obtained data regarding all the above-mentioned techniques was performed using chemometric analysis. This article reviews the literature between 2007 and 2020.

Effect of processing and storage on the volatile profile of sugarcane honey: A four-year study

Sugarcane honey (SCH) is a syrup from Madeira Island recognized by its unique and excellent aroma, associated to volatile organic compounds (VOCs) generated during the well-defined five stages of its traditional making process. The establishment of volatile profile throughout all SCH-making stages during four years, allowed the evaluation of the influence of each stage in the typical characterisitcs of SCH. One hundred eighthy seven VOCs were identified, being associated to several origins and formation pathways. VOCs formed during stage 1 and 2 were originate from raw material, and its oxidation (i.e. enzymatic browning) and thermal degradation (i.e. lipid oxidation, Maillard reactions, Strecker degradation). In stage 3 and 4, the caramelization and melanoidin degradation also occurred, while in stage 5, the thermal degradation continues, followed by microbial activity. Chemometric analysis allowed to identify 35 VOCs as potential markers for processing control by the producers and as guarantee of the typicality and authenticity of SCH. Based on the obtained results, we propose for the first time an innovative schematic diagram explaining the potential reactions and pathways for VOCs formation during the different steps of the SCH production.

Wheat authentication：An overview on different techniques and chemometric methods

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

A Gas Chromatography-Mass Spectrometry Method for the Determination of Fatty Acids and Sterols in Yeast and Grape Juice

Lipids are essential components of all living cells. In an oenological context, the supply of unsaturated lipids in grape juice allows the yeasts to grow and ferment, despite very low levels of oxygen. The current study proposes a systematic optimization procedure for the analysis of fatty acids and sterols relevant to the grape fermentation process, including both extracellular and intracellular (i.e., yeast cells) lipids. Even though it was extensive, the sample preparation yielded reproducible results for all compounds of interest. The stability of the analyzed compounds was also tested to offer some implementation flexibility for the extensive procedure. The performance parameters (i.e., selectivity, linearity, limit of detection and quantitation, accuracy, and precision) indicated that the method was suitable for future practical implementation. The proof of concept also suggests that the list of compounds of interest can be expanded if additional peaks are identified. Given the large variation in concentrations, the dilution of the matrix needs to be carefully considered in order to ensure that the lipids of interest are still within the dynamic range and not below the limit of detection and/or quantification.

Chemical Differentiation of Sugarcane Cultivars Based on Volatile Profile and Chemometric Analysis

Sugarcane (SC) is a perennial grass widely cultivated in tropical and subtropical regions. However, its cultivation in Europe is residual, where Madeira Island, Portugal, is the only region where SC continues to be extensively cultivated. For the first time, the volatile profiles of regional cultivars were established by solid-phase microextraction combined with gas chromatography-mass spectrometry. Different volatile profiles for each cultivar were recognized, identifying 260 volatile organic compounds belonging to 15 chemical classes, such as aldehydes, alcohols, ketones, hydrocarbons, esters, and terpenes. Chemometric analysis procedure, namely, one-way ANOVA with Tukey's test, principal component analysis, partial least-square analysis, linear discriminant analysis, and hierarchical clustering analysis, allowed the differentiation between all regional cultivars. This study represents an important contribution for the maintenance of biodiversity and subsistence of the SC industry in Europe. Furthermore, it is also a valuable contribution to establish the typicality of traditional SC-based products, such as SC honey.

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

Standardization of chromatographic signals - Part II: Expanding instrument-agnostic fingerprints to reverse phase liquid chromatography

There is a large amount of literature relating to multivariate analytical methods using liquid chromatography together with multivariate chemometric/data mining methods in the food science field. Nevertheless, dating the obtained results cannot be compared as they are based on data acquired by a particular analytical instrument, thus they are instrument-dependant. Therefore, this creates difficulties in generating a database large enough to gather together all the variability of the samples. The solution to this problem is to obtain an instrument-agnostic chromatographic signal that is independent of the chromatographic state, i.e., measuring instrument or particular condition of the same instrument from which it was acquired. This paper describes the methodology to be followed to obtain standardized instrumental fingerprints when liquid chromatography is used for prior separation. For this purpose both internal and external chemical standards series are used as references. As an application example, we have applied this methodology for the determination of biophenols in olive oil by liquid chromatography coupled to ultraviolet-visible detector (LC-UV), using three different LC-UV instruments. The instrument-agnostic fingerprints obtained show a high grade of similarity, regardless of the state of the chromatographic system or the time of acquisition.

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea

Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.

Self-Optimizing Support Vector Elastic Net

Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A self-optimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free support vector elastic net that self-optimizes two key regularization constants, i.e., λ for L2 regularization and t for L1 regularization, is developed and referred to as self-optimizing support vector elastic net (SOSVEN). Response surface modeling (RSM) and bootstrapped Latin partitions (BLPs) are incorporated for the optimization. Responses at a set of design points over the ranges of the two factors are evaluated with an internal BLP validation using a calibration set. A 2-dimensional interpolation with a cubic spline fits a response surface to determine the best condition that gives the best-estimated response. The SOSVEN with RSM had comparable performances with the one tuned by grid search, while the RSM is more efficient. The developed SOSVEN was compared with two parameter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vector regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS-DA) and support vector classification (SVC) for classification. For calibration, the SOSVEN with RSM worked equivalently well or better than the other two self-optimizing methods for the evaluations using meat and hemp oil data sets. For classification, a reference wine data set and mass spectra of different marijuana extracts were used. The three classifiers had similar performances to identify the cultivars of wines with nearly 98% of accuracy. The SOSVEN significantly outperformed sPLS-DA and SVC to classify the mass spectra of marijuana extracts with an overall accuracy of 97%. These results demonstrated excellent abilities of SOSVEN for classification and calibration.

Feijoa [Acca sellowiana (Berg) Burret] accessions characterization and discrimination by chemometrics

BACKGROUND

Feijoa [Acca sellowiana (Berg) Burret] is a Brazilian native fruit with few commercial-level plantations and high agroindustrial potential. A genotype evaluation experiment was conducted from 1996 onward, aiming to obtain fruits based on the agronomical parameters; however, the selection based on chemical composition had not been evaluated with respect to developing a new cultivar. Accordingly, the present study aimed to discriminate seven accessions of feijoa in terms of nutritional composition, phenolic compounds and antioxidant activity using multivariate analysis (principal component analysis and multivariate contrast), targeting the potential production of a new cultivar with better nutritional value and high antioxidant capacity.

RESULTS

Feijoa husk presented high content of ashes, lipids, proteins, carbohydrates, phenolic compounds and antioxidant activity compared to feijoa pulp. However, only feijoa pulp was selected to multivariate analysis because it is the fruit edible part. Data variability was explained in 78% and the feijoa pulp accessions were discriminated into four groups related to their characteristics. The accession 5 discrimination can be explained by the high content of ashes, carbohydrates, soluble solids, phenolic compounds and antioxidant activity. Accession 6 was also discriminated by the high content of total acidity, pH and proteins, as well as a low content of soluble solids.

CONCLUSION

Feijoa accessions may be indicated for increasing plant selection via hybridization with the other accessions, aiming to produce new cultivars with better nutritional composition and antioxidant capacity. For example, accession 5 is the most suited fruit for human consumption and is a potential plant with respect to becoming a new cultivar. © 2020 Society of Chemical Industry.

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Discrimination between vegetable oil and animal fat by a metabolomics approach using gas chromatography-mass spectrometry combined with chemometrics

Adulteration of olive oil with the other cheap oils and fats plays an important role in economics and has nutritional benefits. In this work, metabolite profiling was performed using gas chromatography-mass spectrometry to identify and quantify animal fat (lard) adulteration in vegetable oil (olive oil). Principal component analysis could correctly identify and clustering olive oil, sunflower oil, sesame oil, lard, and adulterated samples through the changes in their fatty acid methyl esters (FAMEs) profile. A targeted metabolomics method was then optimized and validated through construction of calibration curves of known FAMSs in olive oil and lard. The method was presented high linearity (R2 > 0.96) and good intra and inter day accuracy and precision (79-101 and 86-102% and 2-7 and 3-7, respectively) for determination of FAMEs. Afterwards the absolute concentration and relative percentage of FAMEs were successfully determined in 12 commercial olive oils and 3 lards samples. Methyl myristate, methyl palmitate, methyl oleate, and methyl stearate were selected as discriminant markers to identify and quantify lard adulteration even at a low level of lard (5%w/w), with errors less than 2% in the comparison of the absolute or relative concentrations of FAMEs using several statistical methods. The proposed methodology allowed us to quantify the FAMEs simultaneously and also could predict small amount of lard in the adulterated olive oil samples.

Profiles of Sugar and Organic Acid of Fruit Juices: A Comparative Study and Implication for Authentication

A comparison of sugar and organic acid profiles among different fruit juices (including apple, pear, peach, grape, sweet cherry, strawberry, and blueberry with various varieties) was performed to assess the possibility for authentication coupled with chemometrics. It was found that the distribution of each sugar and organic acid in juices showed some specific characteristics related to fruit species, despite the fact that great differences in the content existed among different varieties. Sucrose was the most abundant sugar in peach juice, accounting for 58.26–77.11% of the total sugar content. However, in grape, blueberry, and sweet cherry juice, glucose and fructose were the predominant sugars. Pear juice contained the highest level of sorbitol, which contributed to 15.02–43.07% of the total sugar content. Tartaric acid was detected only in grape juice among the seven species of fruit juice, with a proportion of 57.95–89.68% in the total acid content. Malic acid was the predominant organic acid in apple and sweet cherry juice, accounting for 69.92–88.30% and 97.51–98.73% of the total acid content of each species. Citric acid was the predominant organic acid in strawberry and blueberry juice, which contributed to 62.39–83.73% and 73.36–89.56% of the total acid content of each species. With the aid of principal component analysis and linear discriminant analysis (LDA), the juice samples could be successfully classified according to fruit species by using the sugar and/or organic acid composition as analytical data. Combination of sugar and organic acid composition gave the best differentiation of these seven species of juices, with a 100% correct classification rate for both the original and the cross-validation method in LDA. Adding malic/citric into the dataset of the organic acid content may also improve the differentiation effect. Furthermore, the adulteration of sweet cherry juice, blueberry juice, raspberry juice, and grape juice with apple juice, pear juice, or peach juice could also be distinguished from their corresponding pure juices based on sugar and organic acid composition by LDA.

Variation in secondary metabolites in a unique set of tomato accessions collected in Turkey

In this study, 50 tomato landraces grown in Turkey were investigated in terms of their secondary metabolite profiles. Each accession was planted in 2016 and 2017 in 3 replicates in an open field. In this study, color, pH and brix of the fruit samples were measured and an unbiased LCMS-based metabolomics approach was applied. Based on Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the relative abundance levels of >250 metabolites, it could be concluded that fruit size was the most influential to the biochemical composition, rather than the geographical origin of accessions. Results indicated substantial biodiversity in various metabolites generally regarded as key to fruit quality aspects, including sugars; phenolic compounds like phenylpropanoids and flavonoids; alkaloids and glycosides of flavour-related volatile compounds. The phytochemical data provides insight into which Turkish accessions might be most promising as starting materials for the tomato processing and breeding industries.

Acidic hydrolysate fingerprints based on HILIC-ELSD/MS combined with multivariate analysis for investigating the quality of Ganoderma lucidum polysaccharides

In this preliminary study, the acidic hydrolysate fingerprints of polysaccharides based on hydrophilic-interaction chromatography-evaporative light scattering detection-electrospray time-of-flight mass spectrometry (HILIC-ELSD/ESI-TOF/MS) combined with multivariate statistical analysis was developed and applied to investigate the quality of Ganoderma lucidum from different regions. Projection-to-latent-structure discrimination analysis (PLS-DA) could distinguish samples of Zhejiang regions from those of other regions. Orthogonal-projection-to-latent-structure discrimination analysis (OPLS-DA) provided clear discrimination between G. lucidum samples cultivated in Zhejiang and that from other regions, in which Polysaccharides and D-galactose could be considered as candidate biomarkers. In addition, the intraspecific differentiation of G. lucidum was preliminarily investigated with samples from Shaanxi region. They were classified into four groups by PCA and PLS-DA, in which L-rhamnose, D-xylose, L-arabinose, and mannose were considered as potential chemical markers. These preliminary results contributed to our understanding of the variance of polysaccharides in Ganoderma spp. from different geographic origins and the intraspecific differentiation from the same region, which suggest great potential in the quality control of Ganoderma spp.

Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)

In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.

Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques

BACKGROUND

Minor compounds of olive oil could have discriminatory characteristics in the authentication of this product. It was aimed to determine the detailed pigment profiles of Turkish olive oils and use them in differentiation of the samples in comparison to fast, reliable, and environmentally friendly Fourier-transform infrared (FTIR) and ultraviolet (UV)-visible spectroscopic techniques. Pigment contents of 91 olive oils obtained from different locations for two consecutive harvesting years were determined with chromatographic analysis and FTIR and UV-visible spectra of these samples were also obtained. All data were analyzed with orthogonal partial least-squares discriminant analysis to investigate the differentiation ability of these methods with regard to their detailed pigment and spectroscopic profiles.

RESULTS

Pheophytin a (2.78-8.98 mg kg^-1 ) and lutein (1.19-4.07 mg kg^-1 ) were the major pigments in all samples. Pigment profiles provided successful classification of olive oils with respect to their designated origins and harvesting year with average correct classification rates of 97%. UV-visible spectroscopy has quite similar results with pigment profiles in terms of its discriminatory power. In addition, FTIR and fused data were slightly better in discrimination of the samples, and the fused dataset has the highest correct classification rate of 100%.

CONCLUSION

Use of detailed pigment profiles is quite promising in authentication of olive oils. However, UV-visible and FTIR spectroscopic techniques could be reliable alternatives for the same purposes. All of the techniques studied have great potential in 'protected designation of origin' certification studies. © 2020 Society of Chemical Industry.

Authentication of Eucommia ulmoides Seed Oil Using Fourier Transform Infrared and Synchronous Fluorescence Spectroscopy Combined with Chemometrics

Eucommia ulmoides is a traditional Chinese herb whose seeds can be used to produce edible oils. Fourier transform infrared (FTIR) and synchronous fluorescence spectroscopic (SyFS) spectra of Eucommia ulmoides seed oil (EUSO) are lacking. The relevant functional and fluorescent groups were determined by FTIR and SyFS techniques for discriminating adulteration of EUSO, respectively. FTIR and SyFS spectra of EUSO and six common-used vegetable oils were recorded from 4000-400 cm^-1 and 250-700 nm at wavelength interval of 60 nm, respectively. Principal component analysis (PCA), linear discriminant analysis (LDA), cluster analysis (CA) and partial least square (PLS) regression was used for qualitative and quantitative calibration of EUSO adulteration. The FTIR spectral regions of 1429-1377 cm^-1 and 1128-1110 cm^-1 based on PCA, LDA, and CA, and the PCA of SyFS spectral regions of 600-700 nm and 300-500 nm were evaluated for qualitative differentiation of EUSO adulteration. The recognition rate of PCA validation was found to be 100% by FTIR regions. PLS calibration was optimal by the spectral normalization vector treatment in the two FTIR spectral regions and SyFS spectra were combined with characteristic absorption peak area, which can achieve quantitative detection of EUSO adulteration. The two techniques are useful for EUSO adulteration detection at levels down to 1% and 0.48% (w/w), respectively. The results indicated that spectral information obtained by FTIR and SyFS of EUSO can be used for qualitative and quantitative analysis of EUSO adulteration with the advantages of high sensitivity, simplicity, and rapidness.

Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions

Milk samples from 1264 cows in 85 farms were authenticated for different farming-systems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared spectra (FTIRS) and gas-chromatographic fatty-acid (FA) profiles. FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improved the results. All milk samples were processed into ripened model-cheeses, and analyzed by near-infrared-spectrometry (NIRS), by proton-transfer-reaction time-of-flight mass-spectrometry for their volatile organic compound (VOCs) fingerprint and by panel sensory profiling (SENS). Farming-system authentication on cheese samples was less efficient than on milk, but still possible. The instrumental methods yielded similar validation results, better than SENS, and their combination improved the correct classification rate. The efficiency of the different technics was affected by specific farming systems. In conclusion, dairy products could be discriminated for farming-systems with acceptable accuracy, but the methods tested differ in sampling procedure, rapidity and costs.

Approaching Authenticity Issues in Fish and Seafood Products by Qualitative Spectroscopy and Chemometrics

The intrinsically complex nature of fish and seafood, as well as the complicated organisation of the international fish supply and market, make struggle against counterfeiting and falsification of fish and seafood products very difficult. The development of fast and reliable omics strategies based on spectroscopy in conjunction with multivariate data analysis has been attracting great interest from food scientists, so that the studies linked to fish and seafood authenticity have increased considerably in recent years. The present work has been designed to review the most promising studies dealing with the use of qualitative spectroscopy and chemometrics for the resolution of the key authenticity issues of fish and seafood products, with a focus on species substitution, geographical origin falsification, production method or farming system misrepresentation, and fresh for frozen/thawed product substitution. Within this framework, the potential of fluorescence, vibrational, nuclear magnetic resonance, and hyperspectral imaging spectroscopies, combined with both unsupervised and supervised chemometric techniques, has been highlighted, each time pointing out the trends in using one or another analytical approach and the performances achieved.

Using stable isotope signatures to delineate the geographic point-of-origin of Keemun black tea

BACKGROUND

Confirmation of food labeling that claims production in a small geographic region is critical to traceability, quality control and brand protection. In the current study, isotope ratio mass spectrometry (IRMS) was used to generate profiles of δ¹³ C and δ¹⁵ N to determine if the stable isotope signatures of Keemun black tea differ within the three counties that claim production. Other factors (cultivar type, leaf maturity and manufacturing process) were considered for their potential effects.

RESULTS

Both cultivar type and leaf maturity have remarkable impact on the δ¹⁵ N values of tea leaves, and that the cultivar influenced the δ¹³ C values. Keemun black tea from Qimen county could be easily discriminated from samples from Dongzhi and Guichi counties based on δ¹⁵ N signatures. The k-NN model was cross-validated with an accuracy of 91.6%. Environmental factors and/or genotype seem to be the major reasons for δ¹⁵ N differences in Keemun black tea from the selected regions.

CONCLUSION

This article provides a potential effective method to delineate the geographic point-of-origin of Keemun black tea based on δ¹⁵ N signatures. © 2018 Society of Chemical Industry.